Opportunity Information: Apply for G16AS00099

  • The Department of the Interior, Geological Survey in the science and technology and other research and development sector is offering a public funding opportunity titled "Cooperative Ecosystem Studies Unit, Gulf Coast CESU" and is now available to receive applicants.
  • Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 15.808.
  • This funding opportunity was created on Jun 22, 2016.
  • Applicants must submit their applications by Jul 08, 2016. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
  • Each selected applicant is eligible to receive up to $250,000.00 in funding.
  • The number of recipients for this funding is limited to 1 candidate(s).
  • Eligible applicants include: Others (see text field entitled Additional Information on Eligibility for clarification).
Apply for G16AS00099

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Opportunity Summary:

The grant opportunity titled "Cooperative Ecosystem Studies Unit, Gulf Coast CESU" (Funding Opportunity Number G16AS00099) is a discretionary funding announcement issued by the U.S. Department of the Interior, U.S. Geological Survey (USGS), through its Wetland and Aquatic Research Center (WARC). It is offered as a cooperative agreement, meaning the recipient would not simply receive funds to work independently, but would be expected to collaborate closely with USGS scientists and staff as the project is carried out. The program falls under the Science and Technology and other Research and Development activity category (CFDA 15.808) and is aimed at advancing research capabilities that support ecological and environmental science.

At its core, the opportunity is focused on "Big Data Applications" for wildlife detection, recognition, and individual identification, with an emphasis on applying modern computer vision methods to real-world ecological monitoring challenges. The project is meant to help USGS WARC develop and demonstrate advanced computer vision solutions that can address common, fundamental problems encountered when trying to automatically classify and identify wildlife in natural settings. In practice, this type of work typically relates to processing large volumes of imagery or video collected from sources such as remote cameras (camera traps), drones, aircraft surveys, underwater cameras, or other sensor systems, and then using machine learning and pattern recognition techniques to detect animals, determine species, and in more advanced cases distinguish between individual animals.

The announcement is specifically targeted to a CESU Partner within the Gulf Coast Cooperative Ecosystem Studies Unit network, rather than being broadly open to all organizations. The eligibility field is listed as "Others," with the key clarification being that applicants must be CESU partners as described in the full eligibility text of the original posting. CESUs are structured partnerships that connect federal agencies with universities and other research organizations to support applied science, technical assistance, and education that benefit natural resource stewardship. By limiting eligibility to CESU partners, the USGS is signaling that it wants to leverage an established collaborative framework and a partner organization with relevant research capacity, data science expertise, and the ability to coordinate closely with federal scientists.

In terms of scale and competitiveness, the funding opportunity anticipates a single award, with an award ceiling of $250,000. That structure indicates a focused, project-specific investment intended to produce concrete outputs and demonstrations rather than funding multiple small projects. The timeline in the source information shows the opportunity was created on June 22, 2016, with an original closing date of July 8, 2016, suggesting it was a short application window and likely designed for rapid project start or to align with an existing research need at WARC.

Overall, this opportunity is best understood as a targeted research and development collaboration to strengthen USGS WARC's capabilities in automated wildlife monitoring using data-intensive, computer vision-driven approaches. The expected outcome would be improved methods and demonstrable solutions for autonomous detection and identification of wildlife in complex natural environments, supporting broader ecological and environmental studies where large-scale data collection is increasingly common but manual analysis is time-consuming, expensive, and difficult to standardize.

Frequently Asked Questions (FAQs)

What is the title of this grant opportunity?

The opportunity is titled "Cooperative Ecosystem Studies Unit, Gulf Coast CESU."

What is the Funding Opportunity Number (FON)?

The Funding Opportunity Number is G16AS00099.

Which agency is offering this funding opportunity?

This is a discretionary funding announcement issued by the U.S. Department of the Interior, U.S. Geological Survey (USGS).

Which USGS center is involved?

The opportunity is issued through the USGS Wetland and Aquatic Research Center (WARC).

What type of funding instrument is being used?

The award is offered as a cooperative agreement.

What does it mean that this is a cooperative agreement?

It means the recipient is expected to collaborate closely with USGS scientists and staff as the project is carried out, rather than working independently with minimal federal involvement.

What is the main focus of the project?

The core focus is "Big Data Applications" for wildlife detection, recognition, and individual identification, emphasizing modern computer vision methods applied to real-world ecological monitoring.

What kinds of technical approaches are implied by the announcement?

The description points to computer vision, machine learning, and pattern recognition approaches designed to automatically detect animals, classify species, and in more advanced cases identify individual animals.

What problem is this opportunity trying to address?

It is aimed at addressing fundamental challenges in automating wildlife classification and identification in natural environments, especially when dealing with large volumes of imagery or video that are expensive and time-consuming to analyze manually.

What types of data sources are typically relevant to this work?

The announcement describes work that typically relates to processing large volumes of imagery or video collected from sources such as remote cameras (camera traps), drones, aircraft surveys, underwater cameras, or other sensor systems.

What activity category does this opportunity fall under?

It falls under the Science and Technology and other Research and Development activity category.

What is the CFDA number listed for this program?

The CFDA number listed is 15.808.

Who is eligible to apply?

The announcement is specifically targeted to a CESU Partner within the Gulf Coast Cooperative Ecosystem Studies Unit (CESU) network, rather than being broadly open to all organizations.

Is this opportunity open to all organizations?

No. Eligibility is limited to CESU partners within the Gulf Coast CESU network, as described in the full eligibility text of the original posting.

Why is eligibility limited to Gulf Coast CESU partners?

The description indicates USGS wants to leverage an established CESU collaborative framework and work with a partner organization that has relevant research capacity, data science expertise, and the ability to coordinate closely with federal scientists.

How many awards are expected?

The opportunity anticipates a single award.

What is the maximum award amount (award ceiling)?

The award ceiling is $250,000.

When was the opportunity created and when did it close?

The opportunity was created on June 22, 2016, with an original closing date of July 8, 2016.

What does the short application window suggest about the opportunity?

Based on the dates provided, the short window suggests it was intended for a rapid start or to align with an existing research need at USGS WARC.

What kinds of outcomes are expected from the project?

The opportunity is framed as a targeted research and development collaboration intended to produce improved methods and demonstrable solutions for autonomous wildlife detection and identification in complex natural environments.

How does this work support USGS and ecological research more broadly?

The intended results support ecological and environmental studies where large-scale data collection is increasingly common, but manual review is difficult to scale and hard to standardize.

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